{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy.stats import pearsonr\n",
    "from adjustText import adjust_text\n",
    "from scipy import stats\n",
    "from mpl_toolkits.axes_grid1.inset_locator import mark_inset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pop_df=pd.read_csv(r'C:\\Users\\Yasaman\\Downloads\\World_bank_population.csv',skiprows=3)\n",
    "pop_df=pop_df[['Country Code','2003','2019']].dropna()\n",
    "pop_df['2019']=pop_df['2019'].astype(int)\n",
    "possible_countries=pop_df.query(\" `2019` >=1000000\")['Country Code'].values\n",
    "possible_countries=[x.lower() for x in possible_countries]\n",
    "\n",
    "\n",
    "\n",
    "excluded_iso3_codes = [\n",
    "    \"IRL\",  # Ireland\n",
    "    \"SSD\",  # South Sudan\n",
    "    \"SDN\",  # Sudan\n",
    "    \"COG\",  # Republic of the Congo\n",
    "    \"COD\",  # Democratic Republic of the Congo\n",
    "    \"GIN\",  # Guinea\n",
    "    \"GNB\",  # Guinea-Bissau\n",
    "    \"GNQ\",  # Equatorial Guinea\n",
    "    \"PNG\",  # Papua New Guinea\n",
    "    \"XKX\",  # Kosovo (unofficial)\n",
    "    \"MNE\",  # Montenegro\n",
    "    \"SRB\",  # Serbia\n",
    "    \"TLS\"   # Timor-Leste\n",
    "]\n",
    "excluded_iso3_codes=[c.lower() for c in excluded_iso3_codes]\n",
    "\n",
    "possible_iso=list(set(possible_countries)-set(excluded_iso3_codes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df=pd.read_csv(r\"C:\\Users\\Yasaman\\Downloads\\Attention-fractional counting.csv\")\n",
    "df=df[(df['affiliation_country'].isin(possible_iso))&df['country'].isin(possible_iso)]\n",
    "\n",
    "df=df.rename(columns={'year':'Year', 'aggregated_value':'count', 'country':'Mention_country', 'affiliation_country':'Aff_country'})\n",
    "Country_list={'Egypt':'EGY', 'Tunisia':'TUN','Libya':'LBY','Syria':'SYR','Yemen':'YEM','Bahrain':'BHR','Jordan':'JOR','Kuwait':'KWT','Morocco':'MAR','Oman':'OMN'}\n",
    "rev_Country_list={Country_list[key]: key for key in Country_list}\n",
    "abbr=[country.lower() for country in Country_list.values()]\n",
    "country_codes=pd.read_csv(r\"C:\\Users\\Yasaman\\Downloads\\iso3.csv\")\n",
    "country_codes['iso3']=[c.lower() for c in country_codes['iso3']]\n",
    "physical_sciences=[ 'MATH', 'ENGI', 'PHYS', 'COMP', \"MUL\"]\n",
    "df=df[~df['subjarea'].isin(physical_sciences)]\n",
    "map={country_codes.iloc[c]['iso3']: country_codes.iloc[c]['name'] for c in range(len(country_codes))}\n",
    "map['irn']='Iran'\n",
    "map['usa']='USA'\n",
    "map['gbr']='UK'\n",
    "filtered_df=df[(df['Mention_country'].isin(abbr))&(df['Mention_country']!=df['Aff_country'])]\n",
    "\n",
    "before_df=filtered_df[filtered_df['Year'].isin(np.arange(2002, 2011, 1))].groupby(by=['Mention_country','Aff_country']).sum().reset_index()[['count','Mention_country','Aff_country']].rename(columns={'count':'count_before'})\n",
    "after_df=filtered_df[filtered_df['Year'].isin(np.arange(2011, 2020, 1))].groupby(by=['Mention_country','Aff_country']).sum().reset_index()[['count','Mention_country','Aff_country']].rename(columns={'count':'count_after'})\n",
    "compare_df=before_df.merge(after_df, how='outer', on=['Mention_country','Aff_country']).fillna(0)\n",
    "compare_df['count_after']/=9\n",
    "compare_df['count_before']/=9\n",
    "compare_df=compare_df.groupby(['Aff_country'])[['count_before', 'count_after']].sum().reset_index()\n",
    "compare_df['difference']=compare_df['count_after']-compare_df['count_before']\n",
    "compare_df=compare_df.sort_values('difference', ascending=False)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Yasaman\\AppData\\Local\\Temp\\ipykernel_17264\\3634022695.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  selected_df['average_gdp_pc'] = selected_df[average_columns].mean(axis=1, skipna=True)\n"
     ]
    }
   ],
   "source": [
    "country_df=pd.read_csv(r\"C:\\Users\\Yasaman\\Downloads\\World_bank_GDP_per_capita.csv\",skiprows=4)\n",
    "\n",
    "columns =[country_df.columns[1]]+list(np.arange(2002, 2020, 1).astype(str))   # Convert year numbers to strings if columns are named as strings\n",
    "selected_df = country_df[columns]  # Select the columns\n",
    "# Generate the list of column names\n",
    "average_columns = np.arange(2002, 2020, 1).astype(str)\n",
    "\n",
    "# Calculate the row-wise average, ignoring NaN values\n",
    "selected_df['average_gdp_pc'] = selected_df[average_columns].mean(axis=1, skipna=True)\n",
    "\n",
    "\n",
    "\n",
    "selected_df=selected_df[[country_df.columns[1],'average_gdp_pc']]\n",
    "selected_df['Country Code']=selected_df['Country Code'].apply(lambda x:x.lower())\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "country_df=selected_df.merge(compare_df, left_on='Country Code', right_on='Aff_country', how='left').dropna(subset=['Aff_country'])\n",
    "country_df=country_df[country_df['Aff_country'].isin(possible_countries)]\n",
    "\n",
    "\n",
    "country_codes=pd.read_csv(r\"C:\\Users\\Yasaman\\Downloads\\iso3.csv\")\n",
    "country_codes['iso3']=[c.lower() for c in country_codes['iso3']]\n",
    "map={country_codes.iloc[c]['iso3']: country_codes.iloc[c]['name'] for c in range(len(country_codes))}\n",
    "map['irn']='Iran'\n",
    "map['usa']='USA'\n",
    "map['gbr']='UK'\n",
    "map['rus']='Russia'\n",
    "map['syr']='Syria'\n",
    "map['are']='UAE'\n",
    "\n",
    "\n",
    "plot_a_df=country_df.reset_index(drop=True)\n",
    "plot_a_df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "aus\n",
      "che\n",
      "deu\n",
      "dnk\n",
      "esp\n",
      "fra\n",
      "gbr\n",
      "irn\n",
      "ita\n",
      "mys\n",
      "nor\n",
      "qat\n",
      "sau\n",
      "swe\n",
      "usa\n",
      "Pearson Correlation Coefficient: 0.4320775078882054\n",
      "P-value: 7.106536939198457e-08\n",
      "Spearman Correlation Coefficient: 0.6083054269673986\n",
      "P-value: 7.748106013843145e-16\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1=plt.subplots(nrows=1, ncols=1)\n",
    "ins = ax1.inset_axes([0.08,0.61,0.45,0.35])\n",
    "\n",
    "\n",
    "sns.regplot(plot_a_df, x='average_gdp_pc', y='difference' ,marker=\"x\", color=\".2\", line_kws=dict(color=\"#7DE1DF\"),ax=ax1,  scatter_kws={\"zorder\":10}, robust=True)\n",
    "ax1.set_ylabel(r'$\\Delta A_i^*$', fontsize=18)\n",
    "ax1.set_xlabel('Average GDP per capita', fontsize=18)\n",
    "\n",
    "\n",
    "sns.regplot(plot_a_df, x='average_gdp_pc', y='difference' ,marker=\"x\", color=\".2\", line_kws=dict(color=\"#7DE1DF\"),ax=ins,  scatter_kws={\"zorder\":10}, robust=True)\n",
    "ins.set_ylabel(r'', fontsize=10)\n",
    "ins.set_xlabel(r'', fontsize=10)\n",
    "ins.set_xlim(0, 10000)\n",
    "ins.set_ylim(-15/8, 20)\n",
    "\n",
    "\n",
    "points=[]\n",
    "# Annotate each point\n",
    "for line in plot_a_df[(plot_a_df['difference']>30)|(plot_a_df['average_gdp_pc']>50000)].index:\n",
    "\n",
    "    x = plot_a_df.average_gdp_pc[line]\n",
    "    y = plot_a_df.difference[line]\n",
    "    label =map[plot_a_df.Aff_country[line]]\n",
    "    print(plot_a_df.Aff_country[line])\n",
    "    points+=[ax1.text(x, y, label,\n",
    "                    fontsize=8, ha='center', va='center')]\n",
    "\n",
    "adjust_text(points, arrowprops=dict(arrowstyle=\"-\", color='k', lw=0.5, alpha=.5), expand=(1.5, 2.5))        \n",
    "points2=[]\n",
    "# Annotate each point\n",
    "for line in plot_a_df[(plot_a_df['difference']<=20)& (plot_a_df['difference']>8)&(plot_a_df['average_gdp_pc']<10000)].index:\n",
    "\n",
    "    x = plot_a_df.average_gdp_pc[line]\n",
    "    y = plot_a_df.difference[line]\n",
    "    label =map[plot_a_df.Aff_country[line]]\n",
    "    points2+=[ins.text(x, y, label,\n",
    "                    fontsize=8, ha='center', va='center')]\n",
    "    \n",
    "adjust_text(points2, arrowprops=dict(arrowstyle=\"-\", color='k', lw=0.5, alpha=.5), expand=(1.5, 2), ax=ins)        \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "correlation_coefficient, p_value = pearsonr(plot_a_df['difference'], plot_a_df['average_gdp_pc'])\n",
    "print(\"Pearson Correlation Coefficient:\", correlation_coefficient)\n",
    "print(\"P-value:\", p_value)\n",
    "\n",
    "correlation_coefficient, p_value = stats.spearmanr(plot_a_df['difference'], plot_a_df['average_gdp_pc'])\n",
    "print(\"Spearman Correlation Coefficient:\", correlation_coefficient)\n",
    "print(\"P-value:\", p_value)\n",
    "\n",
    "mark_inset(ax1, ins, loc1=2, loc2=4, fc=\"none\", ec=\"0.5\", linestyle=':')\n",
    "\n",
    "fig.savefig('attention_vs_gdp.pdf', bbox_inches='tight')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
